The present invention relates to data analysis, and more specifically, to entity analytics. Entity classification is a key function of many systems and products used for entity resolution and entity relationship recognition. Current entity resolution engine products are typically configured to classify entities according to simple input factors (e.g. an “entity class” code can be required as part of each inbound observation to be handled by the product). These products also can process information about each entity's components; such pieces of information are often known as “features” of those entities. Features are typically classified based on configured “feature class” codes.
However, this way of statically defining entity and feature classes has a few drawbacks. First, the product can be configured for use only on data that arrives from a specific set of sources and that is structured in certain ways. Second, product configuration is complex and requires every entity and feature class that may be anticipated for a given deployment to be defined in advance by specialists trained extensively to configure the product. Third, for a given configuration it is not possible to reclassify entities after the resolution phase, which may limit the resolution engine's ability to provide sensible resolutions. Thus, there is a need for improved entity classification and entity resolution techniques.